Affective Computing – bringing humans and machines closer through emotions
summary
Overview of actionable methods and demos: defines emotion vs feeling vs mood, covers measurement by facial/body expressions, speech prosody, physiological signals, and links to concrete services — Azure Face API returns emotion scores (example: 83% surprise) and Affectiva: 6M faces across 87 countries. Shows sentiment API for review scoring, cites Paul Ekman’s FACS and Duchenne’s 60 expressions, describes deployed uses (retail cameras, medical sensors, Pepper robot) and highlights cross-cultural ethics from the Moral Machine data.
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chapters (25)
- 0:00Start
- 1:58Background Information
- 3:17Terminology
- 4:04Mood and Effect
- 4:19Well-Known Theories of Emotions
- 6:18The Two-Factor Theory of Emotions or the Cognitive Theory of Emotion
- 7:09First Cognitive Appraisal
- 8:05Can We Measure Emotions
- 10:05Three Mains for Emotion Detection
- 10:15Universal Emotions
- 11:08Effective Computing
- 13:16The Motion Api
- 13:41The Face Detection Api
- 13:56Create a New Resource
- 16:24Sentiment Analyze Analysis
- 21:31Use Cases
- 23:00Pepper
- 24:09Narrow Intelligence
- 24:50The Turing Test
- 25:01The Loebner Prize Competition
- 25:28Coffee Test
- 26:06Employment Test
- 29:38Ethical Ai
- 32:41Collective Intelligence
- 33:17The Average Social Perceptiveness
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